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Found 6 papers, 2 papers with code
Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Training
An activation function is an element-wise mathematical function and plays a crucial role in deep neural networks (DNN).
Efficient Adaptive Activation Rounding for Post-Training Quantization
To deal with the runtime overhead, we use a coarse-grained version of the border function.
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements.
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity
Network pruning can reduce the high computation cost of deep neural network (DNN) models.
Adversarial Defense Through Network Profiling Based Path Extraction
Recently, researchers have started decomposing deep neural network models according to their semantics or functions.
Effective Path: Know the Unknowns of Neural Network
Despite their enormous success, there is still no solid understanding of deep neural network’s working mechanism.
